docstr: Create and return the transformation needed for training and inference.
  
  Returns
  -------
  Transformation
      The transformation that will be applied entry-wise to datasets,
      at training and inference time.
docstr_tok: ["Create","and","return","the","transformation","needed","for","training","and","inference",".","Returns","--","--","--","-","Transformation","The","transformation","that","will","be","applied","entry-wise","to","datasets",",","at","training","and","inference","time","."]
code: def create_transformation(self) -> Transformation:
      """
      Create and return the transformation needed for training and inference.
  
      Returns
      -------
      Transformation
          The transformation that will be applied entry-wise to datasets,
          at training and inference time.
      """
      raise NotImplementedError
  
imports: from typing import NamedTuple, Optional
  from functools import partial
  import numpy
  import torch
  import torch.nn
  from torch.utils import data
  from torch.utils.data import DataLoader
  from gluonts.env import env
  from gluonts.core.component import validated
  from gluonts.dataset.common import Dataset
  from gluonts.model.estimator import Estimator
  from gluonts.torch.model.predictor import PyTorchPredictor
  from gluonts.transform import SelectFields, Transformation
  from pts import Trainer
  from pts.model import get_module_forward_input_names
  from pts.dataset.loader import TransformedIterableDataset
label_desc: Use image node_id 2 for calling the PyTorchEstimator obj's underlying member method code with example usage: obj.create_transformation() without return types